Please use this identifier to cite or link to this item:
metadata.artigo.dc.title: AMMI Bayesian models to study stability and adaptability in maize
metadata.artigo.dc.creator: Bernardo Júnior, Luiz Antonio Yanes
Silva, Carlos Pereira da
Oliveira, Luciano Antonio de
Nuvunga, Joel Jorge
Pires, Luiz Paulo Miranda
Von Pinho, Renzo Garcia
Balestre, Marcio
metadata.artigo.dc.subject: Maize - Genetic breeding
Genotypes × environments interaction
Additive main effects and multiplicative interaction
Bayesian analysis
Milho - Melhoramento genético
Interação genótipos × ambientes
Efeitos principais aditivos e interação multiplicativa
Análise Bayesiana
metadata.artigo.dc.publisher: American Society of Agronomy 2018
metadata.artigo.dc.identifier.citation: BERNARDO JÚNIOR, L. A. Y. et al. AMMI Bayesian models to study stability and adaptability in maize. Agronomy Journal Abstract - Biometry, Modeling & Statistics, [S. l.], v. 110, n. 5, p. 1765-1776, 2018.
metadata.artigo.dc.description.abstract: The identification of genotypes presenting wide adaptability and stability is pivotal in breeding programs. To identify such genotypes, it is necessary to use sophisticated analytical tools to establish the genotypes × environments interaction (GEI) pattern across multi-environment trials and select for genotypic stability and adaptability. The aim of the present study was to estimate GEI using Bayesian analysis of Additive Main Effects and Multiplicative Interaction (AMMI) models for both balanced and unbalanced data sets and estimate the predictive ability of model. Two studies were assessed to showcase this approach; in the first, 10 commercial maize (Zea mays) single-cross hybrids and 45 double-cross hybrids were evaluated at 15 different locations. In the second study, 28 hybrids were evaluated in 35 different environments distributed over two different harvest seasons (first and second harvests) with unbalanced data sets within and between harvests. The Bayesian analysis of the AMMI models was robust in dealing with the unbalanced data. This approach is promising for the identification of interaction patterns and the estimation of GEI. The genotypes and environments could be grouped according to their interaction patterns even using the unbalanced data sets, showing that Bayesian analysis of AMMI models could be applied effectively for multi-environment trials. The prediction for missing hybrids was satisfactory in a simulated unbalanced design and captured the GEI and patterns in the data. This allowed the direct comparison of genotypes from the first and second harvests and the estimation of selection gain.
metadata.artigo.dc.language: en_US
Appears in Collections:DES - Artigos publicados em periódicos

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.